For use in computing objective, or task-based, measures of IQ that serve as figures-of-merit (FOM) for breast USCT designs, it is critical that VITs employ numerical breast phantoms (NBPs) that accurately convey the anatomical and acoustic properties of the female breast.
Moreover, it is known that object variability (i.e., patient-to-patient differences in the breast anatomy and properties) can be viewed as a source of randomness present in image data that limits the performance of human or numerical observers on detection or estimation tasks [1]}, [2]}, [3]}.
It is therefore important to have the capability of producing ensembles of NBPs that possess prescribed statistical properties associated with a specified to-be-imaged subject cohort; these NBPs can each be virtually imaged and, subsequently, ensemble-averaged objective IQ measures can be computed for use in assessing and refining USCT imaging technologies.
However, existing NBPs do not satisfy these requirements and are limited
by factors that include oversimplified anatomical structures[4]}, [5]}, [6]}, [7]}, [8]} or are representative of healthy subjects only [9]}, [10]}. NBPs derived from clinical magnetic resonance images are available [9]} but are severely limited in number; as such, they do not accurately depict variability in breast anatomy or acoustic properties that will be present in a prescribed patient cohort.
Other tools for generating NBPs[7]}, [8]} rely on digital templates or segmented clinical images with simplified anatomical structures and consider only a limited number of tissue types. In summary, there remains an important need for developing NBPs for use in VITs of breast USCT that 1) comprise realistic structures and acoustic properties; 2) include lesions and/or other pathologies; and 3) are representative of the stochastic variability in breast size, shape, composition, anatomy, and tissue properties observed in a specified cohort of to-be-imaged subjects.
